In emergency management and disaster response, it depends on how you define predictive analytics. Weather forecasting to some extent is an example: We move assets around and we make decisions based on weather forecasts all the time when we see hurricanes or other potentially severe weather events approaching.
A lot of folks when they hear “predictive analytics,” they’re thinking about predicting or forecasting the probability of a certain event or incident occurring at X place at Y time. It can be very difficult to know with a high degree of certainty exactly where and when a particular event is going to occur. But once an incident occurs, predictive analytics has a lot of promise for helping us identify and forecast the probabilities of the next effects of that event.
For example, the probabilities of an improvised explosive device detonating at the corner of Walk and Don’t Walk can be very difficult to predict — we don’t have a lot of past data on those types of events. But once that event happens, predictive analytics can hold a lot of promise for helping us understand the cascading effects: What are the traffic impacts and what’s going to happen with the surrounding infrastructure if that device affects the adjacent water or power infrastructure, even medical infrastructure, schools and things like that.